Overview

Dataset statistics

Number of variables12
Number of observations3673
Missing cells0
Missing cells (%)0.0%
Duplicate rows475
Duplicate rows (%)12.9%
Total size in memory344.5 KiB
Average record size in memory96.0 B

Variable types

Numeric12

Alerts

Dataset has 475 (12.9%) duplicate rowsDuplicates
residual sugar is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxide and 1 other fieldsHigh correlation
density is highly overall correlated with residual sugar and 3 other fieldsHigh correlation
alcohol is highly overall correlated with chlorides and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-04-17 23:16:07.295071
Analysis finished2023-04-17 23:16:32.685267
Duration25.39 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct66
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8653689
Minimum3.8
Maximum11.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:32.788074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.3
median6.8
Q37.3
95-th percentile8.3
Maximum11.8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84685033
Coefficient of variation (CV)0.12335103
Kurtosis1.2537908
Mean6.8653689
Median Absolute Deviation (MAD)0.5
Skewness0.5915238
Sum25216.5
Variance0.71715548
MonotonicityNot monotonic
2023-04-17T19:16:33.061067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 227
 
6.2%
6.6 227
 
6.2%
6.4 207
 
5.6%
6.7 182
 
5.0%
6.9 180
 
4.9%
6.5 173
 
4.7%
7 171
 
4.7%
7.2 158
 
4.3%
7.1 150
 
4.1%
6.3 144
 
3.9%
Other values (56) 1854
50.5%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
4.2 1
 
< 0.1%
4.4 2
 
0.1%
4.5 1
 
< 0.1%
4.6 1
 
< 0.1%
4.7 3
 
0.1%
4.8 8
0.2%
4.9 5
 
0.1%
5 15
0.4%
5.1 17
0.5%
ValueCountFrequency (%)
11.8 1
 
< 0.1%
10.7 2
 
0.1%
10.3 2
 
0.1%
10.2 1
 
< 0.1%
10 3
0.1%
9.9 2
 
0.1%
9.8 7
0.2%
9.7 4
0.1%
9.6 5
0.1%
9.5 1
 
< 0.1%

volatile acidity
Real number (ℝ)

Distinct118
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27917778
Minimum0.08
Maximum1.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:33.352716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.15
Q10.21
median0.26
Q30.32
95-th percentile0.46
Maximum1.1
Range1.02
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.10155493
Coefficient of variation (CV)0.36376436
Kurtosis5.6748526
Mean0.27917778
Median Absolute Deviation (MAD)0.06
Skewness1.6375236
Sum1025.42
Variance0.010313403
MonotonicityNot monotonic
2023-04-17T19:16:33.558985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 201
 
5.5%
0.24 192
 
5.2%
0.26 181
 
4.9%
0.22 178
 
4.8%
0.2 165
 
4.5%
0.25 155
 
4.2%
0.27 154
 
4.2%
0.23 154
 
4.2%
0.3 142
 
3.9%
0.32 135
 
3.7%
Other values (108) 2016
54.9%
ValueCountFrequency (%)
0.08 3
 
0.1%
0.085 1
 
< 0.1%
0.1 2
 
0.1%
0.105 5
 
0.1%
0.11 12
 
0.3%
0.115 2
 
0.1%
0.12 20
0.5%
0.125 3
 
0.1%
0.13 33
0.9%
0.135 1
 
< 0.1%
ValueCountFrequency (%)
1.1 1
< 0.1%
1.005 1
< 0.1%
0.965 1
< 0.1%
0.93 1
< 0.1%
0.91 1
< 0.1%
0.905 1
< 0.1%
0.85 1
< 0.1%
0.815 1
< 0.1%
0.785 1
< 0.1%
0.78 1
< 0.1%

citric acid
Real number (ℝ)

Distinct83
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33320719
Minimum0
Maximum1.66
Zeros15
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:33.726652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.176
Q10.27
median0.32
Q30.38
95-th percentile0.53
Maximum1.66
Range1.66
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.12012568
Coefficient of variation (CV)0.36051348
Kurtosis7.4801194
Mean0.33320719
Median Absolute Deviation (MAD)0.06
Skewness1.384374
Sum1223.87
Variance0.01443018
MonotonicityNot monotonic
2023-04-17T19:16:33.866694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 225
 
6.1%
0.28 218
 
5.9%
0.32 195
 
5.3%
0.34 168
 
4.6%
0.27 163
 
4.4%
0.26 160
 
4.4%
0.31 160
 
4.4%
0.29 157
 
4.3%
0.49 155
 
4.2%
0.33 139
 
3.8%
Other values (73) 1933
52.6%
ValueCountFrequency (%)
0 15
0.4%
0.01 6
 
0.2%
0.02 5
 
0.1%
0.04 9
0.2%
0.05 2
 
0.1%
0.06 3
 
0.1%
0.07 10
0.3%
0.08 3
 
0.1%
0.09 7
0.2%
0.1 11
0.3%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 5
0.1%
0.91 1
 
< 0.1%
0.88 1
 
< 0.1%
0.82 1
 
< 0.1%
0.81 1
 
< 0.1%
0.8 1
 
< 0.1%
0.79 2
 
0.1%
0.78 2
 
0.1%

residual sugar
Real number (ℝ)

Distinct291
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4379118
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:34.109667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.7
median5.2
Q39.9
95-th percentile16
Maximum65.8
Range65.2
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation5.1399589
Coefficient of variation (CV)0.79838914
Kurtosis4.4348632
Mean6.4379118
Median Absolute Deviation (MAD)3.6
Skewness1.1560717
Sum23646.45
Variance26.419177
MonotonicityNot monotonic
2023-04-17T19:16:34.254536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4 142
 
3.9%
1.2 141
 
3.8%
1.6 129
 
3.5%
1.3 115
 
3.1%
1.1 114
 
3.1%
1.5 100
 
2.7%
1.8 76
 
2.1%
1.7 75
 
2.0%
2 64
 
1.7%
1 64
 
1.7%
Other values (281) 2653
72.2%
ValueCountFrequency (%)
0.6 1
 
< 0.1%
0.7 5
 
0.1%
0.8 16
 
0.4%
0.9 30
 
0.8%
0.95 4
 
0.1%
1 64
1.7%
1.1 114
3.1%
1.15 2
 
0.1%
1.2 141
3.8%
1.25 2
 
0.1%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 2
0.1%
26.05 1
< 0.1%
23.5 1
< 0.1%
22 1
< 0.1%
20.8 2
0.1%
20.7 2
0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%
20.2 2
0.1%

chlorides
Real number (ℝ)

Distinct143
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045594065
Minimum0.009
Maximum0.346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:34.456093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.027
Q10.036
median0.043
Q30.05
95-th percentile0.066
Maximum0.346
Range0.337
Interquartile range (IQR)0.014

Descriptive statistics

Standard deviation0.021181314
Coefficient of variation (CV)0.46456298
Kurtosis41.350006
Mean0.045594065
Median Absolute Deviation (MAD)0.007
Skewness5.143715
Sum167.467
Variance0.00044864808
MonotonicityNot monotonic
2023-04-17T19:16:34.593096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 164
 
4.5%
0.046 142
 
3.9%
0.048 139
 
3.8%
0.036 138
 
3.8%
0.042 135
 
3.7%
0.04 133
 
3.6%
0.034 130
 
3.5%
0.045 129
 
3.5%
0.047 129
 
3.5%
0.05 125
 
3.4%
Other values (133) 2309
62.9%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 1
 
< 0.1%
0.013 1
 
< 0.1%
0.014 3
 
0.1%
0.015 3
 
0.1%
0.016 5
0.1%
0.017 4
 
0.1%
0.018 9
0.2%
0.019 5
0.1%
0.02 11
0.3%
ValueCountFrequency (%)
0.346 1
< 0.1%
0.301 1
< 0.1%
0.29 1
< 0.1%
0.255 1
< 0.1%
0.239 1
< 0.1%
0.212 1
< 0.1%
0.209 1
< 0.1%
0.208 1
< 0.1%
0.204 1
< 0.1%
0.201 1
< 0.1%

free sulfur dioxide
Real number (ℝ)

Distinct123
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.182957
Minimum3
Maximum146.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:34.759261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q123
median34
Q346
95-th percentile63
Maximum146.5
Range143.5
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.812714
Coefficient of variation (CV)0.4778653
Kurtosis1.9441031
Mean35.182957
Median Absolute Deviation (MAD)11
Skewness0.83660011
Sum129227
Variance282.66736
MonotonicityNot monotonic
2023-04-17T19:16:35.126046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 126
 
3.4%
26 97
 
2.6%
34 95
 
2.6%
31 94
 
2.6%
24 93
 
2.5%
36 90
 
2.5%
35 89
 
2.4%
23 87
 
2.4%
22 83
 
2.3%
33 82
 
2.2%
Other values (113) 2737
74.5%
ValueCountFrequency (%)
3 10
 
0.3%
4 9
 
0.2%
5 21
0.6%
6 22
0.6%
7 22
0.6%
8 29
0.8%
9 21
0.6%
10 44
1.2%
11 33
0.9%
11.5 1
 
< 0.1%
ValueCountFrequency (%)
146.5 1
 
< 0.1%
131 1
 
< 0.1%
128 1
 
< 0.1%
124 1
 
< 0.1%
122.5 1
 
< 0.1%
118.5 1
 
< 0.1%
112 1
 
< 0.1%
110 1
 
< 0.1%
108 3
0.1%
105 2
0.1%

total sulfur dioxide
Real number (ℝ)

Distinct241
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.30411
Minimum10
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:35.599850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile74.6
Q1109
median135
Q3167
95-th percentile211
Maximum313
Range303
Interquartile range (IQR)58

Descriptive statistics

Standard deviation42.026785
Coefficient of variation (CV)0.30387227
Kurtosis-0.15491419
Mean138.30411
Median Absolute Deviation (MAD)29
Skewness0.2398208
Sum507991
Variance1766.2506
MonotonicityNot monotonic
2023-04-17T19:16:35.728844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 51
 
1.4%
111 49
 
1.3%
150 46
 
1.3%
117 45
 
1.2%
118 44
 
1.2%
98 42
 
1.1%
132 41
 
1.1%
125 41
 
1.1%
140 40
 
1.1%
126 40
 
1.1%
Other values (231) 3234
88.0%
ValueCountFrequency (%)
10 1
 
< 0.1%
18 2
0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
24 2
0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
28 4
0.1%
29 1
 
< 0.1%
30 1
 
< 0.1%
ValueCountFrequency (%)
313 1
 
< 0.1%
307.5 1
 
< 0.1%
294 1
 
< 0.1%
259 1
 
< 0.1%
256 2
0.1%
253 2
0.1%
252 2
0.1%
251 3
0.1%
249.5 1
 
< 0.1%
249 2
0.1%

density
Real number (ℝ)

Distinct827
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99406572
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:36.007537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.98964
Q10.99176
median0.9938
Q30.99612
95-th percentile0.999048
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00436

Descriptive statistics

Standard deviation0.0030267362
Coefficient of variation (CV)0.003044805
Kurtosis12.602364
Mean0.99406572
Median Absolute Deviation (MAD)0.00212
Skewness1.1849756
Sum3651.2034
Variance9.1611322 × 10-6
MonotonicityNot monotonic
2023-04-17T19:16:36.270883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9928 47
 
1.3%
0.992 42
 
1.1%
0.9932 39
 
1.1%
0.9944 39
 
1.1%
0.9927 38
 
1.0%
0.9934 38
 
1.0%
0.9938 38
 
1.0%
0.9954 37
 
1.0%
0.9948 36
 
1.0%
0.993 35
 
1.0%
Other values (817) 3284
89.4%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.9874 1
< 0.1%
0.98742 1
< 0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
0.1%
0.98815 1
< 0.1%
0.98816 1
< 0.1%
ValueCountFrequency (%)
1.03898 1
< 0.1%
1.0103 2
0.1%
1.00295 1
< 0.1%
1.00241 1
< 0.1%
1.0024 1
< 0.1%
1.00196 1
< 0.1%
1.00182 1
< 0.1%
1.0017 2
0.1%
1.0012 1
< 0.1%
1.00118 1
< 0.1%

pH
Real number (ℝ)

Distinct101
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1893629
Minimum2.72
Maximum3.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:36.626899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.96
Q13.09
median3.18
Q33.28
95-th percentile3.45
Maximum3.82
Range1.1
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.14958267
Coefficient of variation (CV)0.046900486
Kurtosis0.57374115
Mean3.1893629
Median Absolute Deviation (MAD)0.1
Skewness0.44411147
Sum11714.53
Variance0.022374975
MonotonicityNot monotonic
2023-04-17T19:16:37.018724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.14 132
 
3.6%
3.16 130
 
3.5%
3.24 112
 
3.0%
3.19 110
 
3.0%
3.2 105
 
2.9%
3.15 105
 
2.9%
3.08 104
 
2.8%
3.1 103
 
2.8%
3.22 102
 
2.8%
3.12 101
 
2.7%
Other values (91) 2569
69.9%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 1
 
< 0.1%
2.77 1
 
< 0.1%
2.79 3
0.1%
2.8 2
 
0.1%
2.82 1
 
< 0.1%
2.83 2
 
0.1%
2.84 1
 
< 0.1%
2.85 3
0.1%
2.86 7
0.2%
ValueCountFrequency (%)
3.82 1
 
< 0.1%
3.8 1
 
< 0.1%
3.79 1
 
< 0.1%
3.77 2
0.1%
3.76 2
0.1%
3.75 1
 
< 0.1%
3.74 2
0.1%
3.72 3
0.1%
3.7 1
 
< 0.1%
3.69 1
 
< 0.1%

sulphates
Real number (ℝ)

Distinct73
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48954261
Minimum0.22
Maximum1.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:37.179237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.34
Q10.41
median0.47
Q30.55
95-th percentile0.71
Maximum1.06
Range0.84
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.11357413
Coefficient of variation (CV)0.2320005
Kurtosis1.3958823
Mean0.48954261
Median Absolute Deviation (MAD)0.07
Skewness0.95736745
Sum1798.09
Variance0.012899083
MonotonicityNot monotonic
2023-04-17T19:16:37.339840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 193
 
5.3%
0.46 167
 
4.5%
0.38 157
 
4.3%
0.44 156
 
4.2%
0.45 135
 
3.7%
0.42 134
 
3.6%
0.47 129
 
3.5%
0.54 129
 
3.5%
0.43 128
 
3.5%
0.49 128
 
3.5%
Other values (63) 2217
60.4%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 1
 
< 0.1%
0.26 3
 
0.1%
0.27 10
 
0.3%
0.28 10
 
0.3%
0.29 9
 
0.2%
0.3 18
0.5%
0.31 24
0.7%
0.32 37
1.0%
ValueCountFrequency (%)
1.06 1
 
< 0.1%
1 1
 
< 0.1%
0.98 3
0.1%
0.96 2
 
0.1%
0.95 5
0.1%
0.94 2
 
0.1%
0.92 2
 
0.1%
0.9 5
0.1%
0.89 1
 
< 0.1%
0.88 4
0.1%

alcohol
Real number (ℝ)

Distinct92
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.510266
Minimum8
Maximum14.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:37.552706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.9
Q19.5
median10.4
Q311.4
95-th percentile12.7
Maximum14.2
Range6.2
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.2304042
Coefficient of variation (CV)0.11706689
Kurtosis-0.67696331
Mean10.510266
Median Absolute Deviation (MAD)1
Skewness0.49836302
Sum38604.207
Variance1.5138944
MonotonicityNot monotonic
2023-04-17T19:16:37.697976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 174
 
4.7%
9.5 162
 
4.4%
9.2 149
 
4.1%
9 144
 
3.9%
10 127
 
3.5%
10.5 121
 
3.3%
11 115
 
3.1%
10.4 113
 
3.1%
9.1 109
 
3.0%
9.8 107
 
2.9%
Other values (82) 2352
64.0%
ValueCountFrequency (%)
8 1
 
< 0.1%
8.4 2
 
0.1%
8.5 8
 
0.2%
8.6 17
 
0.5%
8.7 60
1.6%
8.8 83
2.3%
8.9 68
1.9%
9 144
3.9%
9.1 109
3.0%
9.2 149
4.1%
ValueCountFrequency (%)
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 3
 
0.1%
13.9 2
 
0.1%
13.8 2
 
0.1%
13.7 7
0.2%
13.6 6
 
0.2%
13.55 1
 
< 0.1%
13.5 9
0.2%
13.4 15
0.4%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8687721
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.8 KiB
2023-04-17T19:16:37.951026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88695892
Coefficient of variation (CV)0.15113194
Kurtosis0.23931479
Mean5.8687721
Median Absolute Deviation (MAD)1
Skewness0.18648555
Sum21556
Variance0.78669612
MonotonicityNot monotonic
2023-04-17T19:16:38.155620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1654
45.0%
5 1099
29.9%
7 639
 
17.4%
8 132
 
3.6%
4 131
 
3.6%
3 13
 
0.4%
9 5
 
0.1%
ValueCountFrequency (%)
3 13
 
0.4%
4 131
 
3.6%
5 1099
29.9%
6 1654
45.0%
7 639
 
17.4%
8 132
 
3.6%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 132
 
3.6%
7 639
 
17.4%
6 1654
45.0%
5 1099
29.9%
4 131
 
3.6%
3 13
 
0.4%

Interactions

2023-04-17T19:16:29.504980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:07.436064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.188699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.928287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.616651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:10.385064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:13.780853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:17.074566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:19.698590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:22.746849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:24.816702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:27.083044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:29.628098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:07.503642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.245383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.989867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.675803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:10.457639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:14.156629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:17.409642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:20.005830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:23.069592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:25.065240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:27.241732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:29.825879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:07.561667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.296809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.043634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.735223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:10.513500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:14.340169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:17.564058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:20.141175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:23.203290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:25.256950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:27.492013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:30.002683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:07.626890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.355193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.098142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.790974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:10.572682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:14.575318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:17.801995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:20.259032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:23.305601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:25.376720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:27.757323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:30.149235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:07.708131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.407700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.155777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.846630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:10.899451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:14.846726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:18.055331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:20.362025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:23.465165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:25.498868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:27.969852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:30.302923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:07.774300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.469678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.213281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.903216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:11.328049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:15.011781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:18.266842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:20.471600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:23.597969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:25.811690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:28.288370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:30.564723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:07.838490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.601614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.272029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.958979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:11.754689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:15.325299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:18.606197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:20.814034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:23.777378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:25.959037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:28.523058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:30.808700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:07.898001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.654846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.327901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:10.101849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:12.016394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:15.972024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:18.819309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:21.146734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:24.024356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:26.094279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:28.714965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:30.982755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:07.954956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.710940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.381688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:10.158256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:12.177117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:16.138353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:18.972080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:21.553624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:24.152031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:26.383672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:28.865000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:31.395963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.020719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.761906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.442102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:10.212622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:12.596959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:16.259067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:19.084883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:21.931787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:24.409381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:26.525649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:29.040055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:31.551629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.076082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.818618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.493026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:10.271667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:13.025752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:16.420897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:19.247853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:22.068058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:24.541471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:26.696969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:29.181095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:31.720018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.132620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:08.873047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:09.560837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:10.325861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:13.424471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:16.728111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:19.392641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:22.419811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:24.682928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:26.933897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:16:29.388992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-17T19:16:38.375493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
fixed acidity1.000-0.0410.3110.0990.088-0.0250.1140.262-0.413-0.007-0.093-0.086
volatile acidity-0.0411.000-0.1330.104-0.009-0.0780.1260.014-0.035-0.0260.032-0.203
citric acid0.311-0.1331.0000.0230.0360.0900.1020.088-0.1430.082-0.0210.006
residual sugar0.0990.1040.0231.0000.2300.3500.4260.780-0.176-0.012-0.447-0.083
chlorides0.088-0.0090.0360.2301.0000.1720.3670.503-0.0490.086-0.565-0.307
free sulfur dioxide-0.025-0.0780.0900.3500.1721.0000.6210.333-0.0010.040-0.2760.033
total sulfur dioxide0.1140.1260.1020.4260.3670.6211.0000.563-0.0080.152-0.475-0.193
density0.2620.0140.0880.7800.5030.3330.5631.000-0.1080.091-0.822-0.346
pH-0.413-0.035-0.143-0.176-0.049-0.001-0.008-0.1081.0000.1360.1470.119
sulphates-0.007-0.0260.082-0.0120.0860.0400.1520.0910.1361.000-0.0430.039
alcohol-0.0930.032-0.021-0.447-0.565-0.276-0.475-0.8220.147-0.0431.0000.439
quality-0.086-0.2030.006-0.083-0.3070.033-0.193-0.3460.1190.0390.4391.000

Missing values

2023-04-17T19:16:32.134250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-17T19:16:32.556779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
06.30.250.223.300.04841.0161.00.992563.160.5010.56
17.80.300.2916.850.05423.0135.00.999803.160.389.06
27.40.380.277.500.04124.0160.00.995353.170.4310.05
37.40.160.491.200.05518.0150.00.991703.230.4711.26
47.20.270.2815.200.0466.041.00.996653.170.3910.96
56.80.270.2813.300.07650.0163.00.997903.030.388.66
68.40.220.308.900.02417.0118.00.994562.990.3410.56
78.10.170.4414.100.05343.0145.01.000603.280.758.88
86.70.200.246.500.04428.0100.00.993483.120.3310.26
96.80.140.181.400.04730.090.00.991643.270.5411.26
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
36637.20.250.2814.400.05555.0205.00.998603.120.389.07
36645.70.260.2510.400.0207.057.00.994003.390.3710.65
36656.40.160.328.750.03838.0118.00.994493.190.4110.75
36667.30.200.392.300.04824.087.00.990442.940.3512.06
36676.70.300.4418.750.05765.0224.00.999563.110.539.15
36686.20.210.526.500.04728.0123.00.994183.220.499.96
36697.00.140.329.000.03954.0141.00.995603.220.439.46
36707.60.270.523.200.04328.0152.00.991293.020.5311.46
36716.30.240.2913.700.03553.0134.00.995673.170.3810.66
36728.10.270.351.700.03038.0103.00.992553.220.6310.48

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
2617.00.150.2814.700.05129.0149.00.997922.960.399.077
3437.30.190.2713.900.05745.0155.00.998072.940.418.887
3667.40.190.3114.500.04539.0193.00.998603.100.509.266
195.70.220.2016.000.04441.0113.00.998623.220.468.965
3937.60.200.3014.200.05653.0212.50.999003.140.468.985
205.70.220.2216.650.04439.0110.00.998553.240.489.064
666.20.230.3617.200.03937.0130.00.999463.230.438.864
686.20.250.547.000.04658.0176.00.994543.190.7010.454
1506.60.220.2317.300.04737.0118.00.999063.080.468.864
1796.70.160.3212.500.03518.0156.00.996662.880.369.064